Welcome!

This website provides information for the paper Estimating Neighborhood Asking Rents using Scraped Craigslist Rental Listings. The goal of this research is to a.) understand the temporal dynamics of rent estimates in Seattle and b.) forecast the current quarter’s rent levels based off of the prior periods. Throughout the following analyses, the training data are 2017 Q2 up to the prior quarter (i.e. 2018 Q1). The test period is a forecast for the current period and includes comparison to the appropriate median rent estimates for data observed so far in this period.

Most graphics include some level of interactivity, usually either hover-over tooltip information or a slider to control various views of the graphic. You can download the source code and data for this project from Github here

Contact Chris Hess at hesscl@uw.edu for more information about this report.

This page was last updated: 2018-05-22

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Table of Contents

• Distribution: density graphic to investigate the distribution of rents among Seattle neighborhoods for each quarter
• Panel Time-Series: line graphic to show the observed or modeled temporal structure
• Spatial Time-Series: series of maps to show observed change across time
• Model fit: tables of model root mean square error (RMSE), mean absolute error (MAE), and deviance information criterion (DIC) across training and test data

Observed vs. Smoothed Rent Estimates

Model legend

• int - Quarter fixed intercept
• ns - Non-spatial tract random effect for each tract, quarter fixed intercept
• nsar1 - Non-spatial tract random effect model, AR(1) random effect for quarter, i.i.d. random effect for quarter
• bym - Spatial intrinsic conditional autoregressive (ICAR) tract random-effect with quarter fixed intercept
• spt - Spatial intrinsic conditional autoregressive (ICAR) tract random-effect, AR(1) random effect for quarter, i.i.d. random effect for quarter and tract-quarter

RMSE

train_test	int_rmse	ns_rmse	nsar1_rmse	bym_rmse	spt_rmse
Test	307.0736	214.1802	206.5359	206.2087	198.5532
Training	324.3490	136.1696	136.9117	138.2680	137.8658

MAE

train_test	int_mae	ns_mae	nsar1_mae	bym_mae	spt_mae
Test	248.1144	160.51160	149.92773	155.44384	144.77186
Training	258.1041	89.43893	90.29539	92.75487	92.95742

DIC

int_DIC	ns_DIC	nsar1_DIC	bym_DIC	spt_DIC
-168.1242	-679.9015	-679.7958	-685.7156	-686.1196